AI Analysis
Final verdict: SUSPICIOUS
The package shows no immediate signs of malicious activity but has a high metadata risk due to missing repository and maintainer history, suggesting potential unreliability.
- High metadata risk due to missing repository and maintainer history
- No detected network or shell risks
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network interactions.
- Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
- Metadata: The package is suspicious due to the lack of repository and maintainer history, indicating potential unreliability.
Package Quality Overall: Low (3.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
1 test file(s) detected (e.g. test_package.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (7645 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
98 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Alina" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with alina-mypackage
Create a fully functional mini-app that predicts housing prices based on various features using the 'alina-mypackage' library. Your app will serve as a simple yet powerful tool for real estate enthusiasts and professionals who want to estimate house values without deep knowledge of machine learning. Here’s a step-by-step guide to building this application: 1. **Data Collection**: Begin by collecting data on housing prices. This dataset should include features such as square footage, number of bedrooms, bathrooms, location, year built, and other relevant attributes. 2. **Data Preprocessing**: Utilize 'alina-mypackage' to preprocess your dataset. Perform tasks like handling missing values, scaling numerical features, encoding categorical variables, and splitting the dataset into training and testing sets. 3. **Model Selection**: Choose a model from 'alina-mypackage' to predict housing prices. Start with a simple linear regression model and then experiment with more complex models like K-Nearest Neighbors (KNN) and neural networks provided by the package. 4. **Training the Model**: Train each selected model using the training dataset. Use 'alina-mypackage' to fit the models and ensure you understand the parameters being used. 5. **Evaluation**: Evaluate the performance of each model using appropriate metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared value. Use 'alina-mypackage' diagnostics tools to gain insights into model performance. 6. **User Interface**: Develop a simple user interface where users can input housing features and receive predicted prices. This can be a basic command-line interface or a web-based form depending on your preference and skills. 7. **Deployment**: Once satisfied with your model's performance and the user interface, deploy your application. Consider hosting it on a cloud platform if it’s web-based. Throughout the development process, make sure to document your steps, decisions, and any challenges faced. This project not only showcases your ability to use 'alina-mypackage' but also demonstrates practical machine learning application in a real-world scenario.
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